Well File:
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- Well Plat ( results)
- Wellbore Schematic ( results)
- Directional Survey ( results)
- Fluid Sample ( results)
- Log ( results)
- Density ( results)
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Richard Zemel
Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, Richard Zemel
Neural Information Processing Systems http://nips.cc/
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling
Neural Information Processing Systems http://nips.cc/
Dualing GANs
Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel
Neural Information Processing Systems http://nips.cc/
Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Neural Information Processing Systems http://nips.cc/
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph Neural Network models.
Learning Latent Subspaces in Variational Autoencoders
Jack Klys, Jake Snell, Richard Zemel
Variational autoencoders (VAEs) [10, 20] are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to interpret or control. We consider the problem of unsupervised learning of features correlated to specific labels in a dataset. We propose a VAE-based generative model which we show is capable of extracting features correlated to binary labels in the data and structuring it in a latent subspace which is easy to interpret. Our model, the Conditional Subspace VAE (CSVAE), uses mutual information minimization to learn a low-dimensional latent subspace associated with each label that can easily be inspected and independently manipulated. We demonstrate the utility of the learned representations for attribute manipulation tasks on both the Toronto Face [23] and CelebA [15] datasets.
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
David Madras, Toni Pitassi, Richard Zemel
Neural Information Processing Systems http://nips.cc/
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K. Duvenaud, Raquel Urtasun, Richard Zemel
Neural Information Processing Systems http://nips.cc/
Learning Latent Subspaces in Variational Autoencoders
Jack Klys, Jake Snell, Richard Zemel
Neural Information Processing Systems http://nips.cc/
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Neural Information Processing Systems http://nips.cc/